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Jun 19, 2019
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updated class_weight explanation #14121

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updated class_weight explanation
arnaudstiegler Jun 18, 2019
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glossary_class_weight
arnaudstiegler Jun 19, 2019
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7 changes: 6 additions & 1 deletion doc/glossary.rst
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Expand Up @@ -1412,7 +1412,12 @@ functions or non-estimator constructors.
``class_weight='balanced'`` can be used to give all classes
equal weight by giving each sample a weight inversely related
to its class's prevalence in the training data:
``n_samples / (n_classes * np.bincount(y))``.
``n_samples / (n_classes * np.bincount(y))``. Class weights will be
used differently depending on the algorithm: for linear models (such
as linear SVM or logistic regression), the class weights will alter the
loss function by weighting the loss of each sample by its class weight.
For tree-based algorithms, the class weights will be used for
reweighting the splitting criterion.
**Note** however that this rebalancing does not take the weight of
samples in each class into account.

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